• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1272-1276
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• 2009

극치강우사상에 의한 설계 홍수량의 갑작스런 증 감은 홍수, 가뭄과 같은 기상학적 요인에 기인한 재난을 발생시킨다. 많은 연구자들은 보다 정확한 확률강우량의 예측과 유출량의 예측을 위해 많은 노력을 하고 있다. 본 연구에서는 강원도 강릉 강우관측소를 대상으로 강우-빈도곡선의 불확실성 분석을 수행하였다. 관측 자료의 수집에서 발생하는 불확실성을 최소화 하고자 ARMA 모형을 이용하여 합성강우자료를 구축하였으며, 발생된 합성강우량을 Bootstrap 방법을 이용하여 대규모의 자료집단으로 발생시킴으로서 신뢰구간에 사용할 자료집단을 발생시켰다. 본 연구에서는 극치강우사상에 적합한 것으로 알려진 Gumbel 분포와 일반극치 분포(GEV 분포) 모형을 선정하였으며 각 확률분포모형에 대한 매개변수 추정방법으로 최우도법, 확률가중모멘트법 그리고 베이지안 추론방법을 사용하여 각 매개변수의 최후 추정치를 산정하였다. 또한 원 자료를 이용하여 최우도법, 확률가중모멘트법 그리고 베이지안 추론방법을 통해 매개변수를 산정 후 강우-빈도 곡선을 추정하여 합성강우자료의 Bootstrap 방법에 의해 발생된 자료로부터 산정한 강우-빈도 곡선의 신뢰구간과 비교함으로서 불확실성이 낮은 확률강우량을 산정할 수 있는 매개변수 추정방법을 평가하고자하였다.

• The Korean Journal of Financial Management
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• v.22 no.2
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• pp.43-69
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• 2005

This study provides empirical evidence that the stochastic beta model based on Bayesian analysis outperforms the existing conditional beta model and GARCH model in terms of the estimation accuracy and the explanatory power in the cross-section of stock returns in Korea. Betas estimated by the stochastic beta model explain $30{\sim}50%$ of the cross-sectional variation in stock-returns, whereas other time-varying beta models account for less than 3%. Such a difference in explanatory power across models turns out to come from the fact that the stochastic beta model absorbs the variation due to the market anomalies such as size, BE/ME, and idiosyncratic volatility. These results support the rational asset pricing model in that market anomalies are closely related to the variation of expected returns generated by time-varying betas.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.155-163
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• 2019

The knowledge about drought propagation is very important in accurate estimation of hydrological drought characteristics and efficient development of early warning system. This study investigated a probabilistic relationship of drought propagation based on Bayesian network model for historic period and for future projection under climate change scenario RCP 8.5 over the Han River basin. The results revealed that the propagation rate and lag time have increasing and decreasing trends from the historic period of 1967-2013 to the future periods of 2014-2053 and 2054-2100 under climate change, respectively. The probabilistic results of Bayesian model revealed that the probability of occurrence of lag time varied spatially and decreased when the intensity of meteorological drought changed from moderate to severe and extreme condition during 1967-2013. The values of probability increased in the first future period of 2014-2053 in several sub-basins and slight decreased in the second period of 2054-2100. The proposed probabilistic results will be useful for the decision makers to develop related policies with an appropriate insight toward the future drought status.

• Journal of KIISE
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• v.44 no.11
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• pp.1149-1155
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• 2017

Recently, with the development of communication technology, it has become possible to collect various sensor data that indicate the environmental stimuli within a space. In this paper, we propose a group emotion prediction system using a modular Bayesian network that was designed considering the psychological impact of environmental stimuli. A Bayesian network can compensate for the uncertain and incomplete characteristics of the sensor data by the probabilistic consideration of the evidence for reasoning. Also, modularizing the Bayesian network has enabled flexible response and efficient reasoning of environmental stimulus fluctuations within the space. To verify the performance of the system, we predict public emotion based on the brightness, volume, temperature, humidity, color temperature, sound, smell, and group emotion data collected in a kindergarten. Experimental results show that the accuracy of the proposed method is 85% greater than that of other classification methods. Using quantitative and qualitative analyses, we explore the possibilities and limitations of probabilistic methodology for predicting group emotion.

• Journal of KIISE:Software and Applications
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• v.31 no.12
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• pp.1591-1601
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• 2004

Clustering, a technique for the analysis of the genes, organizes the patterns into groups by the similarity of the dataset and has been used for identifying the functions of the genes in the cluster or analyzing the functions of unknown gones. Since the genes usually belong to multiple functional families, fuzzy clustering methods are more appropriate than the conventional hard clustering methods which assign a sample to a group. In this paper, a Bayesian validation method is proposed to evaluate the fuzzy partitions effectively. Bayesian validation method is a probability-based approach, selecting a fuzzy partition with the largest posterior probability given the dataset. At first, the proposed Bayesian validation method is compared to the 4 representative conventional fuzzy cluster validity measures in 4 well-known datasets where foray c-means algorithm is used. Then, we have analyzed the results of Saccharomyces cell cycle expression data evaluated by the proposed method.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.94-96
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• 2004

에이전트의 '물체 따라가기'는 전통적으로 자동운전이나 가이드 등의 다양한 서비스를 제공할 수 있는 기본적인 기능이다. 여러 가지 물체가 있는 환경에서 '물체 따라가기'를 하기 위해서는 목적하는 대상이 어디에 있는지 찾을 수 있어야 하며, 실제 환경에는 사람이나 차와 같이 움직이는 물체들이 존재하기 때문에 다른 물체들을 피할 수 있어야 한다. 그런데 에이전트의 최적화된 피하기 행동은 장애물의 모양과 크기에 따라 다르게 생성될 수 있다. 본 논문에서는 다양한 모양과 크기의 장애물이 있는 환경에서 최적의 피하기 행동을 생성하면서 물체를 추적하기 위해 반응형 에이전트의 행동선택을 강화학습 한다. 여기에서 정확하게 상태를 인식하기 위하여 상태를 추론하고 목표물과 일정거리를 유지하기 위해 베이지안 추론을 이용한다 베이지안 추론은 센서정보를 이용해 확률 테이블을 생성하고 가장 유력한 상황을 추론하는데 적합한 방법이고, 강화학습은 실시간으로 장애물 종류에 따른 상태에서 최적화된 행동을 생성하도록 평가함수를 제공하기 때문에 베이지안 추론과 강화학습의 결합모델로 장애물에 따른 최적의 피하기 행동을 생성할 수 있다. Webot을 이용한 시뮬레이션을 통하여 다양한 물체가 존재하는 환경에서 목적하는 대상을 따라가면서 이종의 움직이는 장애물을 최적화된 방법으로 피할 수 있음을 확인하였다.

• Korean Journal of Construction Engineering and Management
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• v.17 no.5
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• pp.13-21
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• 2016

Insolvency of construction companies that play the role of main contractors can lead to clients' losses due to non-fulfillment of construction contracts, and it can have negative effects on the financial soundness of construction companies and suppliers. The construction industry has the cash flow financial characteristic of receiving a project and getting payment based on the progress of the construction. As such, insolvency during project progress can lead to financial losses, which is why the prediction of construction companies is so important. The prediction of insolvency of Korean construction companies are often made through the KMV model from the KMV (Kealhofer McQuown and Vasicek) Company developed in the U.S. during the early 90s, but this model is insufficient in predicting construction companies because it was developed based on credit risk assessment of general companies and banks. In addition, the predictive performance of KMV value's insolvency probability is continuously being questioned due to lack of number of analyzed companies and data. Therefore, in order to resolve such issues, the Bayesian Probabilistic Approach is to be combined with the existing insolvency predictive probability model. This is because if the Prior Probability of Bayesian statistics can be appropriately predicted, reliable Posterior Probability can be predicted through ensured conditionality on the evidence despite the lack of data. Thus, this study is to measure the Expected Default Frequency (EDF) by utilizing the Bayesian Probabilistic Approach with the existing insolvency predictive probability model and predict the accuracy by comparing the result with the EDF of the existing model.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.2
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• pp.100-109
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• 2007

Object recognition of service robots is very important for most of services such as delivery, and errand. Conventional methods are based on the geometric models in static industrial environments, but they have limitations in indoor environments where the condition is changable and the movement of service robots occur because the interesting object can be occluded or small in the image according to their location. For solving these uncertain situations, in this paper, we propose the method that exploits observed objects as context information for predicting interesting one. For this, we propose the method for modeling domain knowledge in probabilistic frame by adopting Bayesian networks and ontology together, and creating knowledge model dynamically to extend reasoning models. We verify the performance of our method through the experiments and show the merit of inductive reasoning in the probabilistic model

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.79-90
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• 2004

In this study, we applied the Bayesian Networks for the case of the trip generation models using the Seoul metropolitan area's house trip survey Data. The household income was used for the independent variable for the explanation of household size and the number of cars in a household, and the relationships between the trip generation and the households' social characteristics were identified by the Bayesian Networks. Furthermore, trip generation's characteristics such as the household income, household size and the number of cars in a household were also used for explanatory variables and the trip generation model was developed. It was found that the Bayesian Networks were useful tool to overcome the problems which were in the traditional trip generation models. In particular the various transport policies could be evaluated in the very short time by the established relationships. It is expected that the Bayesian Networks will be utilized as the important tools for the analysis of trip patterns.

• Journal of Korea Water Resources Association
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• v.50 no.11
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• pp.769-779
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• 2017

As the occurrence of drought is recently on the rise, the reliable drought forecasting is required for developing the drought mitigation and proactive management of water resources. This study developed a probabilistic hydrological drought forecasting method using the Bayesian Networks and drought propagation relationship to estimate future drought with the forecast uncertainty, named as the Propagated Bayesian Networks Drought Forecasting (PBNDF) model. The proposed PBNDF model was composed with 4 nodes of past, current, multi-model ensemble (MME) forecasted information and the drought propagation relationship. Using Palmer Hydrological Drought Index (PHDI), the PBNDF model was applied to forecast the hydrological drought condition at 10 gauging stations in Nakdong River basin. The receiver operating characteristics (ROC) curve analysis was applied to measure the forecast skill of the forecast mean values. The root mean squared error (RMSE) and skill score (SS) were employed to compare the forecast performance with previously developed forecast models (persistence forecast, Bayesian network drought forecast). We found that the forecast skill of PBNDF model showed better performance with low RMSE and high SS of 0.1~0.15. The overall results mean the PBNDF model had good potential in probabilistic drought forecasting.